Image-Text-to-Text
MLX
Safetensors
English
Chinese
visionpsy_nano
vlm
vision-language-model
apple-silicon
siglip2
smollm2
conversational
Instructions to use KaedeTai/VisionPsy-Nano-460M-Flash-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use KaedeTai/VisionPsy-Nano-460M-Flash-MLX with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("KaedeTai/VisionPsy-Nano-460M-Flash-MLX") config = load_config("KaedeTai/VisionPsy-Nano-460M-Flash-MLX") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<|im_start|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "global_image_token": "<|global_image|>", | |
| "image_token": "<|image|>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_max_length": 8192, | |
| "model_specific_special_tokens": { | |
| "global_image_token": "<|global_image|>", | |
| "image_token": "<|image|>", | |
| "r1c1": "<row_1_col_1>", | |
| "r1c2": "<row_1_col_2>", | |
| "r1c3": "<row_1_col_3>", | |
| "r1c4": "<row_1_col_4>", | |
| "r1c5": "<row_1_col_5>", | |
| "r1c6": "<row_1_col_6>", | |
| "r1c7": "<row_1_col_7>", | |
| "r1c8": "<row_1_col_8>", | |
| "r2c1": "<row_2_col_1>", | |
| "r2c2": "<row_2_col_2>", | |
| "r2c3": "<row_2_col_3>", | |
| "r2c4": "<row_2_col_4>", | |
| "r2c5": "<row_2_col_5>", | |
| "r2c6": "<row_2_col_6>", | |
| "r2c7": "<row_2_col_7>", | |
| "r2c8": "<row_2_col_8>", | |
| "r3c1": "<row_3_col_1>", | |
| "r3c2": "<row_3_col_2>", | |
| "r3c3": "<row_3_col_3>", | |
| "r3c4": "<row_3_col_4>", | |
| "r3c5": "<row_3_col_5>", | |
| "r3c6": "<row_3_col_6>", | |
| "r3c7": "<row_3_col_7>", | |
| "r3c8": "<row_3_col_8>", | |
| "r4c1": "<row_4_col_1>", | |
| "r4c2": "<row_4_col_2>", | |
| "r4c3": "<row_4_col_3>", | |
| "r4c4": "<row_4_col_4>", | |
| "r4c5": "<row_4_col_5>", | |
| "r4c6": "<row_4_col_6>", | |
| "r4c7": "<row_4_col_7>", | |
| "r4c8": "<row_4_col_8>", | |
| "r5c1": "<row_5_col_1>", | |
| "r5c2": "<row_5_col_2>", | |
| "r5c3": "<row_5_col_3>", | |
| "r5c4": "<row_5_col_4>", | |
| "r5c5": "<row_5_col_5>", | |
| "r5c6": "<row_5_col_6>", | |
| "r5c7": "<row_5_col_7>", | |
| "r5c8": "<row_5_col_8>", | |
| "r6c1": "<row_6_col_1>", | |
| "r6c2": "<row_6_col_2>", | |
| "r6c3": "<row_6_col_3>", | |
| "r6c4": "<row_6_col_4>", | |
| "r6c5": "<row_6_col_5>", | |
| "r6c6": "<row_6_col_6>", | |
| "r6c7": "<row_6_col_7>", | |
| "r6c8": "<row_6_col_8>", | |
| "r7c1": "<row_7_col_1>", | |
| "r7c2": "<row_7_col_2>", | |
| "r7c3": "<row_7_col_3>", | |
| "r7c4": "<row_7_col_4>", | |
| "r7c5": "<row_7_col_5>", | |
| "r7c6": "<row_7_col_6>", | |
| "r7c7": "<row_7_col_7>", | |
| "r7c8": "<row_7_col_8>", | |
| "r8c1": "<row_8_col_1>", | |
| "r8c2": "<row_8_col_2>", | |
| "r8c3": "<row_8_col_3>", | |
| "r8c4": "<row_8_col_4>", | |
| "r8c5": "<row_8_col_5>", | |
| "r8c6": "<row_8_col_6>", | |
| "r8c7": "<row_8_col_7>", | |
| "r8c8": "<row_8_col_8>" | |
| }, | |
| "pad_token": "<|im_end|>", | |
| "processor_class": "VisionPsyNanoProcessor", | |
| "r1c1": "<row_1_col_1>", | |
| "r1c2": "<row_1_col_2>", | |
| "r1c3": "<row_1_col_3>", | |
| "r1c4": "<row_1_col_4>", | |
| "r1c5": "<row_1_col_5>", | |
| "r1c6": "<row_1_col_6>", | |
| "r1c7": "<row_1_col_7>", | |
| "r1c8": "<row_1_col_8>", | |
| "r2c1": "<row_2_col_1>", | |
| "r2c2": "<row_2_col_2>", | |
| "r2c3": "<row_2_col_3>", | |
| "r2c4": "<row_2_col_4>", | |
| "r2c5": "<row_2_col_5>", | |
| "r2c6": "<row_2_col_6>", | |
| "r2c7": "<row_2_col_7>", | |
| "r2c8": "<row_2_col_8>", | |
| "r3c1": "<row_3_col_1>", | |
| "r3c2": "<row_3_col_2>", | |
| "r3c3": "<row_3_col_3>", | |
| "r3c4": "<row_3_col_4>", | |
| "r3c5": "<row_3_col_5>", | |
| "r3c6": "<row_3_col_6>", | |
| "r3c7": "<row_3_col_7>", | |
| "r3c8": "<row_3_col_8>", | |
| "r4c1": "<row_4_col_1>", | |
| "r4c2": "<row_4_col_2>", | |
| "r4c3": "<row_4_col_3>", | |
| "r4c4": "<row_4_col_4>", | |
| "r4c5": "<row_4_col_5>", | |
| "r4c6": "<row_4_col_6>", | |
| "r4c7": "<row_4_col_7>", | |
| "r4c8": "<row_4_col_8>", | |
| "r5c1": "<row_5_col_1>", | |
| "r5c2": "<row_5_col_2>", | |
| "r5c3": "<row_5_col_3>", | |
| "r5c4": "<row_5_col_4>", | |
| "r5c5": "<row_5_col_5>", | |
| "r5c6": "<row_5_col_6>", | |
| "r5c7": "<row_5_col_7>", | |
| "r5c8": "<row_5_col_8>", | |
| "r6c1": "<row_6_col_1>", | |
| "r6c2": "<row_6_col_2>", | |
| "r6c3": "<row_6_col_3>", | |
| "r6c4": "<row_6_col_4>", | |
| "r6c5": "<row_6_col_5>", | |
| "r6c6": "<row_6_col_6>", | |
| "r6c7": "<row_6_col_7>", | |
| "r6c8": "<row_6_col_8>", | |
| "r7c1": "<row_7_col_1>", | |
| "r7c2": "<row_7_col_2>", | |
| "r7c3": "<row_7_col_3>", | |
| "r7c4": "<row_7_col_4>", | |
| "r7c5": "<row_7_col_5>", | |
| "r7c6": "<row_7_col_6>", | |
| "r7c7": "<row_7_col_7>", | |
| "r7c8": "<row_7_col_8>", | |
| "r8c1": "<row_8_col_1>", | |
| "r8c2": "<row_8_col_2>", | |
| "r8c3": "<row_8_col_3>", | |
| "r8c4": "<row_8_col_4>", | |
| "r8c5": "<row_8_col_5>", | |
| "r8c6": "<row_8_col_6>", | |
| "r8c7": "<row_8_col_7>", | |
| "r8c8": "<row_8_col_8>", | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>", | |
| "vocab_size": 49152 | |
| } | |